Results 261 to 270 of about 812,319 (279)

A non-iterative posterior sampling algorithm for linear quantile regression model

Communications in Statistics - Simulation and Computation, 2016
ABSTRACTIn this article, a non-iterative posterior sampling algorithm for linear quantile regression model based on the asymmetric Laplace distribution is proposed. The algorithm combines the inverse Bayes formulae, sampling/importance resampling, and the expectation maximization algorithm to obtain independently and identically distributed samples ...
Fengkai Yang, Haijing Yuan
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Frequentist validity of posterior quantiles for a two-parameter exponential family

Biometrika, 1996
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sun, Dongchu, Ye, Keying
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Frequentist validity of posterior quantiles in the presence of a nuisance parameter: Higher order asymptotics

Biometrika, 1993
Summary: Given a random sample from a distribution with density function that depends on an unknown parameter \(\theta=(\theta_ 1,\theta_ 2)'\), we are concerned with frequentist validity, up to \(o(n^{-1})\), of posterior quantiles of \(\theta_ 1\) treating \(\theta_ 2\) as a nuisance parameter.
Mukerjee, Rahul, Dey, Dipak K.
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Estimating consistent rainfall design values for Norway using Bayesian inference and post-processing of posterior quantiles

2022
<p>The necessity of reliable rainfall statistics for the planning and design of infrastructure is growing as climate change leads to more frequent heavy rainfall events. These rainfall statistics are often displayed as Intensity-Duration-Frequency (IDF) curves, which depict the rainfall intensity (return level) that can be expected at a ...
Julia Lutz, Lars Grinde, Anita Verpe Dyrrdal, Thea Roksvåg, Thordis L. Thorarinsdottir   +4 more
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A Bayesian approach to computing posterior distribution and quantile functions

Journal of Statistical Planning and Inference, 2000
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On the approximate frequentist validity of the posterior quantiles of a parametric function: results based on empirical and related likelihoods

TEST, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chang, In Hong, Mukerjee, Rahul
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Posterior computations based on sample quantiles: one- and two-parameter exponential cases

Statistics, 2007
Statistical analyses based on incomplete ordered samples are common in many practical fields. Some data are unknown or disregarded due to restrictions on data collection, experimental difficulties or negligence. In the interest of compressing the data, only several informative order statistics are considered.
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Posterior Contraction and Credible Interval Coverage for Quantile Neural Networks

Generative Bayesian computation replaces posterior simulation by supervised learning of a transport map from a base measure to the posterior. When the map is a conditional quantile function estimated by a deep network trained under check loss, the method is density free and requires only the ability to simulate.
Nicholas G. Polson, Vadim Sokolov
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Posterior Consistency of Bayesian Quantile Regression Under a Mis-Specified Likelihood Based on Asymmetric Laplace Density

2012
We provide a theoretical justification for the widely used and yet only empirically verified approach of using Asymmetric Laplace Density(ALD) in Bayesian Quantile Regression. We derive sufficient conditions for posterior consistency of the quantile regression parameters even if the true underlying likelihood is not ALD, by considering both the case of
Karthik Sriram, R. Ramamoorthi, Pulak Ghosh   +2 more
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RANGES OF POSTERIOR PROBABILITIES FOR QUASIUNIMODAL PRIORS WITH SPECIFIED QUANTILES

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